Brain-inspired computing is a revolution in computing that is already seeing applications in a myriad of areas, from image recognition to developing learning rules that allow computers to intelligently process big data sets. This field is inspired by the human brain, which is very efficient at certain tasks. For example, the brain can recognize a face or voice using a million times less power than a modern supercomputer. But, so far machine learning has largely focused on restructuring how the computer is put together, but where the building blocks themselves are silicon transistors.

There are many pressing problems today where data-intensive tasks are needed to be accomplished in real time. This can range from sequencing DNA, to self-driving cars recognizing a person walking by, to predicting the trajectory of a flying object. In these examples, traditional computing faces a performance wall where the computing time and energy is severely limited by memory access.

Many technological breakthroughs are enabled by the discovery of new materials. Once new materials are synthesized, their new properties and potential applications are revealed via careful and advanced characterization measurements. This project funds the development of a unique optical characterization instrument to study a wide range of quantum materials and magnetic materials. The instrument enables multiple types of measurements of light-matter interaction at the same area of samples placed in a magnetic field and at low temperatures.

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This NSF CAREER project aims to improve the size and efficiency of electric energy conversion systems which are critical in many applications which progress science, advance national prosperity, and secure national defense. The project will bring transformative change by allowing power converters to operate ten times faster and therefore store ten times less energy, reducing size and improving efficiency. This will be achieved by advancing the science of magnetic components (inductors and transformers) which are the most significant bottleneck to this improved speed and size.

Probabilistic computing is a computing paradigm that can solve certain problems more efficiently than traditional digital computing. While digital computing deals with deterministic binary bits that are either 0 or 1, probabilistic computing deals with probabilistic bits (p- bits) that are sometimes 0 and sometimes 1. This is distinct from quantum computing that deals with quantum bits (q-bits) which are a superposition of 0 and 1 (and hence a mixture of both 0 and 1 all the time).

Emerging materials have novel behaviors that create new opportunities for information processing, especially if the natural behavior of the new materials can be leveraged, rather than trying to engineer them to behave like today’s electronics. In particular, atomically thin materials, also known as two-dimensional (2D) materials, can be naturally ambipolar, i.e. can conduct both electrons and holes. 2D materials also have additional properties, such as hysteresis and variability, that are usually suppressed when used for microelectronics.